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  <div class="section" id="numpy-ma-median">
<h1>numpy.ma.median<a class="headerlink" href="#numpy-ma-median" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.ma.median">
<code class="sig-prename descclassname">numpy.ma.</code><code class="sig-name descname">median</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">overwrite_input=False</em>, <em class="sig-param">keepdims=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/ma/extras.py#L641-L721"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.ma.median" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the median along the specified axis.</p>
<p>Returns the median of the array elements.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Input array or object that can be converted to an array.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">int, optional</span></dt><dd><p>Axis along which the medians are computed. The default (None) is
to compute the median along a flattened version of the array.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary.</p>
</dd>
<dt><strong>overwrite_input</strong><span class="classifier">bool, optional</span></dt><dd><p>If True, then allow use of memory of input array (a) for
calculations. The input array will be modified by the call to
median. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. Note that, if <em class="xref py py-obj">overwrite_input</em> is True, and the input
is not already an <em class="xref py py-obj">ndarray</em>, an error will be raised.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.10.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>median</strong><span class="classifier">ndarray</span></dt><dd><p>A new array holding the result is returned unless out is
specified, in which case a reference to out is returned.
Return data-type is <em class="xref py py-obj">float64</em> for integers and floats smaller than
<em class="xref py py-obj">float64</em>, or the input data-type, otherwise.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="numpy.ma.mean.html#numpy.ma.mean" title="numpy.ma.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>Given a vector <code class="docutils literal notranslate"><span class="pre">V</span></code> with <code class="docutils literal notranslate"><span class="pre">N</span></code> non masked values, the median of <code class="docutils literal notranslate"><span class="pre">V</span></code>
is the middle value of a sorted copy of <code class="docutils literal notranslate"><span class="pre">V</span></code> (<code class="docutils literal notranslate"><span class="pre">Vs</span></code>) - i.e.
<code class="docutils literal notranslate"><span class="pre">Vs[(N-1)/2]</span></code>, when <code class="docutils literal notranslate"><span class="pre">N</span></code> is odd, or <code class="docutils literal notranslate"><span class="pre">{Vs[N/2</span> <span class="pre">-</span> <span class="pre">1]</span> <span class="pre">+</span> <span class="pre">Vs[N/2]}/2</span></code>
when <code class="docutils literal notranslate"><span class="pre">N</span></code> is even.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">),</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">1.5</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">6</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">2.5</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">overwrite_input</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">masked_array(data=[2.0, 5.0],</span>
<span class="go">             mask=[False, False],</span>
<span class="go">       fill_value=1e+20)</span>
</pre></div>
</div>
</dd></dl>

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